Research on Vehicle Type Recognition Based on YOLOv8
In order to reduce traffic hazards,improve the reliability and safety of the transportation sys-tem,and ensure the real-time and accuracy of vehicle detection.This article adopts the YOLOv8 algorithm for object detection in the field of deep learning,which is further enhanced and improved based on YOLOv5.In the Backbone section,a C2f structure with richer gradient flow is used,the Head section is replaced with the current mainstream decoupling head structure,and the loss func-tion uses Task Aligned Assignor positive and negative sample matching method,among other changes.Taking the dataset of dividing vehicles into 7 types as an example for vehicle detection,the dataset is first split and normalized,relevant parameters are adjusted,and finally the model is trained.The experimental results show that the YOLOv8 algorithm achieves 95％mAP in vehicle type recognition on the experimental dataset.This method has good results in vehicle type detec-tion and can be applied in practical transportation systems.
万方数据
维普
